Method for dixon MRI, multi-contrast imaging and multi-parametric mapping with a single multi-echo gradient-recalled echo acquisition

ABSTRACT

To perform Dixon MRI, generate multi-contrast images, and extract multi-parametric maps, this invention presents a multi-echo gradient echo protocol with two sets of echo trains. An example implementation of the invention at 3 T acquires a short-TE train (ΔTE˜1.2 ms, TE&lt;10 ms), which is used to map B0 inhomogeneity and proton density fat fraction (FF), and a second—susceptibility sensitive—long-TE train (16 ms&lt;TE&lt;45 ms) will enable quantification of local frequency shift (LFS) and susceptibility. The presented pipeline automatically generates co-registered images and maps with/without fat-suppressed, including magnitude- and complex-based FF map, B0 map, anatomical images, brain mask, R2* map, unwrapped phase maps for each echo, susceptibility-sensitive images (SWI, LFS and quantitative susceptibility) for each echo, mean susceptibility-sensitive images for each echo-train. The invention is directly applicable to whole head/neck, liver, knee or even whole body scans with sliding table.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of U.S. Provisional application Ser.No. 62/470,164, filed Mar. 10, 2017. The content of the priorapplication is incorporated herein by reference in its entirety.

TECHNICAL FIELD

This disclosure relates to magnetic resonance imaging (MR),particularly, generating Dixon MRI, multi-contrast imaging andmulti-parametric mapping by processing the signals collected with asingle multi-echo gradient echo acquisition.

BACKGROUND

Multi-echo gradient echo (mGRE) sequences have been widely adapted inclinical and scientific practice for different purposes due to theircapability of generating multi-contrast images and extractingmulti-parametric maps. Using the magnitude mGRE data, R2* relaxivity(R2*=1/T2*) mapping techniques (1) have been used to quantify bloodoxygenation level dependent functional MRI, detect and track ofsuper-magnetic iron oxides, visualize abnormalities of the articularknee, assess iron content in brain, liver and heart. However, a fewnewer techniques have been developed using the commonly discarded phasedata. Among these, susceptibility weighted imaging (SWI) (2) uses thephase information to enhance the susceptibility contrast of themagnitude image to visualize veins, microbleeds, hemorrhage, clot, etc;local frequency shift (LFS) mapping techniques (3) characterizeanatomical structures based on phase-contrast features; relying on theLFS maps, quantitative susceptibility mapping (QSM) techniques (4) havealso been explored to quantify susceptibility (χ), which is an intrinsicproperty of materials (e.g., iron, calcium, biological tissue). Also, afew mGRE-based Dixon MRI techniques (5-7) have been developed to jointlyestimate B0 inhomogeneity, proton density fat-fraction (FF), R2* and χ,using the complex mGRE data.

The mGRE sequences can be prescribed to cover a large volume inclinically acceptable scan times when the acquisition is conducted usingmulti-channel RF coils with a large number coil-elements and employingparallel imaging techniques. Compared to 2D multi-slice acquisitions, 3DmGRE acquisition strategies can also generate high-resolution (≤1 mm)data sets with higher SNR (8) and have been proven to be useful for manyapplications, such as stroke, oncology, multiple sclerosis, etc.However, the performance of the R2*, SWI, LFS and QSM techniques forimaging large volumes might be downgraded by many confounding factors,particularly, the presence of macroscopic B0 inhomogeneity and fatcontent (9). A popular approach to avoid the effects of fat on themeasured R2* is to acquire only “in-phase” echoes, i.e., the prescribedecho times (TEs) lead the phase differences caused by chemical shiftbetween fat and water to be equal to a multiple of 2π. However, the“in-phase” approach is based on a single-peak fat model, despite thefact that the fat spectrum has many peaks. If an accurate B0 map isknown, the effect of B0 on the measured R2* could be compensated.Because macroscopic B0 inhomogeneity is also the dominant source of themeasured phase signal, an accurate B0 map is crucial for the accuratebackground-phase removal performed for all phase-sensitive techniques(Dixon MRI, SWI, LFS and QSM). Furthermore, an accurate B0 map enableselimination of the phase component caused by chemical shift when fat ispresent.

In most of the reported work (10), a set of TEs optimized for DixonMill, i.e., short first TE and small echo spacing (ΔTE), was used fordata acquisition. U.S. Patent Application 2014/0142417 A1 discloses amethod of using the separated water and fat images, as well as B0inhomogeneity map to estimate the QSM values from Dixon Mill data. U.S.Patent Application 2015/0002148 A1 discloses a method of jointestimating fat-water fraction and the QSM values by iteratively refiningfat chemical shift.

However, a set of long echo times (TE) with large ΔTE may be needed tomatch the tissue T2* and optimize susceptibility effects for some cases,e.g., brain imaging using R2*, SWI, LFS and QSM. While the concept ofacquiring mGRE images with two echo trains has been consideredpreviously (11), no process has been developed which fully optimizes theacquisition parameters, and corrects for the effect of B0 on thephase-sensitive images.

BRIEF SUMMARY OF THE INVENTION

To address the aforementioned challenge, the present invention collectssignals with a single mGRE sequence with variable echo spacing withineach TR that contains both a short ΔTE echo train to capture water-fatand B0 phase shifts (for FF and B0 mapping) and a longer ΔTE echo train(and long echo times) to capture subtle susceptibility variations andR2* information.

The present invention corrects the phase errors associated with bipolaracquisition for performing Dixon MRI in order to map FF and B0inhomogeneity. Then, the present invention uses the derived B0 map toaddress the challenges during phase processing for whole-head QSM andSWI, such as background removal and phase unwrapping.

The invention presents a fully automated post-processing pipeline. Theinvention automatically generates co-registered images and maps asfollows: 1) magnitude-based and complex-based FF map; 2) B0 map; 3)anatomical images with/without fat-suppression; 4) brain mask; 5) R2*from the short-TE train and R2* from all echoes with FF compensation; 6)unwrapped phase maps for each echo except the first echo; 7) SWI, LFSand QSM maps with/without fat-suppression for each echo except the firstecho; 8) mean SWI, LFS and QSM with/without fat-suppression for theshort- and long-TE trains separately.

The invention is directly applicable to whole head/neck, liver, knee oreven whole body scans with sliding table.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described, by way of example only, withreference to the drawings, in which:

FIG. 1 plots the phase shift caused by chemical-shift between fat andwater vs. echo times.

FIG. 2a is a flowchart illustrating a method of an example embodiment ofthe present disclosure.

FIG. 2b is a flowchart illustrating a method of an example embodiment ofphase error correction of the present disclosure.

FIG. 2c is a flowchart illustrating a method of an example embodiment ofgenerating fat-suppressed anatomical image of the present disclosure.

FIG. 2d is a flowchart illustrating a method of an example embodiment ofphase unwrapping of the present disclosure.

FIG. 3 shows the results of phase-error correction and Dixon MRI fromSubject #1: (a) FF maps without phase error correction; (b) and (c) arethe FF and field (unit in Hz) maps with phase error correction,respectively. For each row, from left to right are the central slices inthe three different orientations.

FIG. 4 shows the results of fat-water separation of five representativeaxial images from Subject #1: (a) anatomical images (b) water-only(i.e., fat-suppressed) and (c) fat-only.

FIG. 5 shows the results of R2* mapping from Subject #1: (a) and (b) arethe R2* maps (unit in 1/s) reconstructed from the first echo train andall echoes, respectively.

FIG. 6 shows the results of phase unwrapping from Subject #1: (a) phasemaps at the first echo; (b) phase maps at the 7^(th) echo; (c) phase ofHermitian product of the 7^(th) and the first echoes (φ_(7,1)); (d)phase maps after B0 removal; (e) the final unwrapped phase maps.

FIG. 7 shows the results of SWI from Subject #1: (a) the high-passfiltered phase maps; (b) the magnitude images; (c) the SWI images bycombining FIGS. 7a and 7b ; (d) the echo-combined SWI image generated byaveraging the images shown in FIG. 7c ; (e) Minimum intensity projectionover 10 mm obtained for the echo-combined SWI.

FIG. 8 shows the results of LFS and QSM from Subject #1: (a) anatomicalimages; (b) and (c) are the mean LFS maps (in units of Hz) obtained fromthe first echo train and the second echo train, respectively; (d) and(e) are the QSM maps (in units of ppm) derived from FIGS. 8b and 8c ,respectively.

FIG. 9 shows the example results from Subject #2: (a) field maps (inunits of Hz); (b) FF maps; (c) fat-suppressed anatomical images; (d) R2maps (in units of 1/s); (e) QSM maps (in units of ppm) obtained for thelate echo train.

FIG. 10 shows the example results from Subject #3: (a) field maps (inunits of Hz); (b) FF maps; (c) fat-suppressed anatomical images; (d) R2maps (in units of 1/s); (e) QSM maps (unit in ppm) obtained from thesecond echo train.

DETAILED DESCRIPTION

Acquisition Protocol

The 3D multi-echo GRE protocol 100 was optimized for a 3 T scanner,based on a scan-time constraint of ˜5 min and a spatial resolution of1.0×1.0×2.0 mm³ over the whole head (and neck). The mGRE protocolincludes two sets of echo trains: the first five echoes (short-TE train)were selected with TEs optimized for Dixon MRI (3.3, 4.7, 6.2, 7.7 and9.5 ms); the late five echoes (long-TE train) were designed with TEsoptimized for susceptibility mapping (16.8, 23.9, 31.1, 38.2, and 45.4ms) while keeping fat and water approximately in-phase. FIG. 1 shows thephase evolution of a fat-only voxel (FF=1) at these selected TEs. Here asix-peak fat model (12) was used to calculate the chemical shift(CS)-related phase values as follows:φ_(CS)(FF,TE_(j))=angle((1−FF)+FFΣ_(m=1) ^(M) a _(m) e ^(i2πΔf) ^(m)^(TE) ^(j) ),  [1]where a_(m) is the known relative intensity of the m^(th) peak of thefat spectrum, Δf_(m) is the corresponding relative frequency shift fromwater, and M (=6) is the total number of peaks of the multi-peak fatmodel. To ensure optimal TEs for the short-TE train, bipolar readoutgradients were used.Flowchart of the Post-Processing Pipeline

The proposed pipeline 200 is summarized in the flowchart of FIG. 2. Step204 loads the collected multi-echo complex data. Step 210 corrects thephase errors associated with the bipolar acquisition over all echoes. Inprocedure 212, the phase error, θ_(bi), can be estimated from the firstthree echoes according to:θ_(bi)=unwrap(angle((S _(2,bi) ×S* _(3,bi))×(S _(2,bi) ×S*_(1,bi))))/4,  [2]where S_(j,bi) is the complex MRI signal at the j^(th) echo usingbipolar acquisition.

In procedure 214, the final θ is obtained by fitting a first orderpolynomial to the 3D volume of the unwrapped phase, θ_(bi).

In procedure 216, the phase-error-corrected complex signal S_(j) iscalculated as follows:S _(j) =S _(j,bi) ×e ⁽⁻¹⁾ ^(j) ^(×θ).  [3]The data sets resulting from the procedure described in Eq. [3] areready for use with established fat-water separation and QSM algorithms,as if they were acquired using unipolar gradients.

Procedure 222 of Step 220 processes the short-TE train data using theB0-NICE method (13), which generates a B0 field (Δf_(B0)) map, afat-water R2* map, a complex-based FF map and a magnitude-based FF map.The procedure 224 generates fat-suppressed anatomical images bymultiplying the averaged magnitude images of the first echo-train by(1−FF).

Step 230 processes the magnitude signal of all echoes to generate an R2*map. We compute the R2* for each voxel by fitting an exponential decaycurve:|S _(j) |=S ₀ ×F _(CS)(FF,TE_(j))×e ^(−R*) ² ^(TE) ^(j) ,  [4]where S₀ is the apparent proton density at TE=0; F_(CS) is theCS-related F-function and is defined as follow,F _(CS)(FF,TE_(j))=|(1−FF)+FF(Σ_(m=1) ^(M) a _(m) e ^(i2πΔf) ^(m) ^(TE)^(j) )|,  [4a]where FF is determined from Step 220. Note that an R2* map based on theshort-TE train only can be calculated, as well as one using all echoes.

Step 240 performs phase unwrapping of the phase signal of all echoes. Tomitigate the issue related to imperfections in channel combination andB1-related phase components, the Hermitian products (HP) between thelate echoes and the first echo are calculated 242 prior to mapping theLFS and QSM,S _(j,1) =S _(j) ×S* ₁.  [5]

To reduce the difficulty of spatial phase unwrapping, the phase termderived from the B0 map (Step 220) was removed on an echo-by-echo basis244 as follows:S _(j,1,b0) S _(j,1) e ^(−2πΔf) ^(B0) ^(×(TE) ^(j) ^(−TE) ¹ ⁾.  [6]

Procedure 246 generates the unwrapped phase as follows:φ_(tmp)=unwrap(angle(S _(j,1,b0)))+2πΔf _(B0)×(TE_(j)−TE₁).  [7]

Because Step 220 involves blurring by a boxcar filter, which does notpreserve edges, procedure 248 derives the edge-recovered final unwrappedphase for each echo as follows:φ_(j)=(angle(S _(j,1))−2π×round((angle(S _(j,1))−φ_(tmp))/2π).  [8]

Step 250 combines the phase and magnitude information to generatesusceptibility weighted images (SWI) images on an echo-by-echo basis(j>1). The unwrapped phase map is high-pass filtered by applying 2DGaussian filter (σ=7 mm) (14). A phase mask is generated by setting thepositive phase values to 1, the phase values less than −π to 0, and theothers linearly converted to the range of (0 1]. The phase-mask isapplied a number of times.

Step 260 generates local frequency shift (LFS) and QS maps for each echo(j>1). The LFS map is generated using the Laplacian boundary value (LBV)method (15) for each individual echo. QS maps are generated byperforming dipole inversion from the LFS maps using the thresholdedk-space division (TKD) method (16) (threshold value=0.19).

Experiements

Three healthy volunteers were scanned under a protocol approved by thelocal research Ethics Board. All exams were conducted on a 3 T scanner(Prisma, Siemens, Erlangen Germany) using a 64-channel head/neck coil.Scanning parameters were: flip angle 15°; TR 51 ms; flow compensation ofthe first echo; readout along anterior/posterior direction; readoutbandwidth 1015 Hz/pixel; spatial resolution 1.0×1.0×2.0 mm³;acceleration factor=2. For Subject #1 and #2, the acquisition matrix wasequal to 224×168×80 for covering whole head within ˜5 minutes scanningtime; for Subject #3, the acquisition matrix was equal to 224×168×96 forcovering the whole head and upper neck within ˜6 minutes.

Data were processed off-line by the fully automatic approach implementedin MATLAB (MathWorks, Natick, Mass.). Channel-combined complex imageswere obtained from the scanner.

Phase unwrapping (Step 210 and 240) was achieved using the PURORunwrapping algorithm (17).

Performing background phase removal for generating the brain LFS map(Step 260) requires a binary brain mask, which is used to identify braintissues. The current implementation determined the mask using themagnitude-based FF maps determined from Step 220, because they are notsensitive to phase errors. Specifically, the FF maps were filtered usingthe 3D Gaussian filter (σ=5 mm), followed by generating the mask bythresholding the filtered FF maps (threshold value=0.25).

EXAMPLES

FIGS. 3 to 8 show the intermediate and the final results from Subject #1as an example of how each step of the pipeline processes the bipolarmGRE data. FIGS. 3 and 4 show the fat-water separation results (Step220). Without applying phase error correction (i.e., if Step 210 isskipped), the calculated FF maps (FIG. 3a ) were not uniform throughoutthe brain, as expected. The success of Step 210 is demonstrated by theimproved quality of the FF images of FIG. 3b , which have a uniformdistribution of FF values throughout the brain and no fat-water swapsare present in the brain or neck regions, while flow artifacts areobservable as suggested by arrows.

FIG. 4 shows a few examples when using the afore-determined FF map togenerate water-only (FIG. 4b ) and fat-only (FIG. 4c ) images from theanatomical images (FIG. 4a ). These results suggest that uniform fatsignal suppression of the anatomical images is obtained; slices coveringthe region around the optic nerve were selected because fat suppressionis commonly required when imaging this area. The arrows point to regionscorrupted by large B0 inhomogeneity.

FIG. 5 shows the R2* reconstruction results (Step 220 and Step 230). TheR2* maps estimated from the short-TE train from the B0-NICE method (FIG.5a ) are very noisy because the TEs (<10 ms) are very short compared tothe tissue T2* values. As expected, the quality of the R2* maps (FIG. 5b) improved significantly when all echoes were included in theprocessing.

FIG. 6 shows the intermediate results of processing the phase signal(Step 240). Starting with the unprocessed phase of the first (FIG. 6a )and seventh (FIG. 6b ) echoes, the calculated Hermitian product (FIG. 6c) demonstrates a reduced number of phase wraps. After the phase shiftcaused by the B0 inhomogeneity (from FIG. 3c ) is removed by Eq. [6],phase images that are nearly wrap-free are seen in FIG. 6d . Lastly,FIG. 6e shows the final unwrapped phase images, which are used as inputfor the SWI and QSM calculations. The arrows in FIGS. 6a and 6b point tothe regions, where the smooth spatial phase variance is B1-related,which is removed by calculating HP. The arrows in FIG. 6d point toregions corrupted by phase wraps.

FIG. 7 shows the multi-echo SWI reconstruction results (Step 250). Thehigh-pass filtered phase images (FIG. 7a ) suggest that the Gaussianfilter removes the background phase successfully. However, artifacts areobservable in regions with rapid phase changes (arrows in FIG. 7a ),especially, at the later echoes. In the same regions signal loss(arrows), which becomes progressively worse with increasing echo time,is clearly seen in the magnitude images of FIG. 7b . FIG. 7c shows theSWI images calculated for each of the five late echoes; these weregenerated by combining the phase of FIG. 7a and magnitude of FIG. 7b .As expected, with longer TE, an increase in venographic contrast isobtained, but a decrease in SNR due to dephasing is also found. Theecho-combined SWI (FIG. 7d ) maintains a better image quality in termsof SNR and CNR compared to the individual-echo SWIs. FIG. 7e is a 10-mmminimum intensity projection (mIP), where additional enhancement ofvessel conspicuity is seen.

FIG. 8 shows the anatomical images and the multi-echo QSM (Step 260)reconstruction results. Arrows in FIG. 8a point to the big bloodvessels. FIGS. 8b and 8c are the mean LFS maps for the short- andlong-TE trains, respectively. These results indicate that the LBV methodsuccessfully performs background removal on an echo-by-echo basis.Comparison between FIGS. 8b and 8c shows that the late echo LFS mapshave higher signal-to-noise ratio and contrast-to-noise ratio. Artifactsdue to large vessels (arrows in FIG. 8a ) are present in both short- andlong-TE-train LFS maps. FIGS. 8d and 8e are the QSM maps derived fromthe mean LFS maps over the short- and the long-TE trains, respectively.Although the simple TKD (truncated k-space division) approach was usedin this work, no streaking artifacts are observed, even in the sagittaland coronal planes.

To demonstrate the robustness of the presented processing pipeline,FIGS. 9 and 10 show the multi-parametric maps generated from the imagesacquired of Subjects #2 and #3, respectively. Note that for all subjectsthe quality of the maps remains consistent over the entire axial extentof the head. For subject 3, an additional 32 mm of neck were included inthe scan to further demonstrate the effectiveness of the algorithms(FIG. 10).

Applications

The example results show that the proposed protocol and processingpipeline have the capability of performing Dixon MRI, multi-contrastimaging and multi-parametric mapping from a single multi-echo GRE scan.The Dixon Mill provides the FF and B0 maps. The FF maps determined canbe used for: 1) fat suppression of the multi-contrast images andmulti-parametric maps, which is very import when imaging lipid-richregions and/or when contrast agent is used; 2) skull segmentation; 3)Dixon attenuation correction for PET/MRI. The B0 maps can be used forthe following example applications: 1) background phasecorrection/removal; 2) image distortion correction; 3) shimming, andothers.

The SWI, LFS and QSM maps obtained for each echo or were echo-averaged(i.e. values of these maps for the short and long echo trains—or allechoes—were averaged to increase SNR). The afore-mentioned maps can beused to investigate both normal tissues and the changes in tissue invarious pathological conditions, which may only be identified from theTE-dependent effect. Fusing the two R2* maps—one from the short-TEtrain, the other from all echoes—will be useful in cases where tissueswith short and long T2s coexist.

It is important to note that while whole brain data is used todemonstrate the invention, the process is directly applicable to otherbody parts, such as liver, knee or even whole body scans with slidingtable.

REFERENCES

-   1. Yablonskiy D A, Sukstanskii A L, Luo J, Wang X. Voxel spread    function method for correction of magnetic field inhomogeneity    effects in quantitative gradient-echo-based MRI. Magn Reson Med    2013; 70(5):1283-1292.-   2. Haacke E M, Xu Y, Cheng Y C, Reichenbach J R. Susceptibility    weighted imaging (SWI). Magn Reson Med 2004; 52(3):612-618.-   3. Duyn J H, van Gelderen P, Li T Q, de Zwart J A, Koretsky A P,    Fukunaga M. High-field MRI of brain cortical substructure based on    signal phase. Proc Natl Acad Sci USA 2007; 104(28):11796-11801.-   4. Liu J, Liu T, de Rochefort L, et al. Morphology enabled dipole    inversion for quantitative susceptibility mapping using structural    consistency between the magnitude image and the susceptibility map.    Neuroimage 2011; 59(3):2560-2568.-   5. Sharma S D, Hernando D, Horng D E, Reeder S B. Quantitative    susceptibility mapping in the abdomen as an imaging biomarker of    hepatic iron overload. Magn Reson Med 2015; 74(3):673-683.-   6. Dimov A V, Liu T, Spincemaille P, et al. Joint estimation of    chemical shift and quantitative susceptibility mapping (chemical    QSM). Magn Reson Med 2015; 73(6):2100-2110.-   7. Cronin M J, Wang N, Decker K S, Wei H, Zhu W Z, Liu C. Exploring    the origins of echo-time-dependent quantitative susceptibility    mapping (QSM) measurements in healthy tissue and cerebral    microbleeds. Neuroimage 2017; 149:98-113.-   8. Hodel J, Leclerc X, Khaled W, et al. Comparison of 3D multi-echo    gradient-echo and 2D T2*MR sequences for the detection of arterial    thrombus in patients with acute stroke. European radiology 2014;    24(3):762-769.-   9. Hernando D, Vigen K K, Shimakawa A, Reeder S B. R*(2) mapping in    the presence of macroscopic B(0) field variations. Magn Reson Med    2012; 68(3):830-840.-   10. Reeder S B, Pineda A R, Wen Z, et al. Iterative decomposition of    water and fat with echo asymmetry and least-squares estimation    (IDEAL): application with fast spin-echo imaging. Magn Reson Med    2005; 54(3):636-644.-   11. Kozawa K S F, Wehrli F W, Takahashi M, Luna H. Quantatitive    measurement of bone marrow composition using multi-echo    gradient-echo sequence and 3-point Dixon processing. ISMRM 2000.-   12. Yu H, Shimakawa A, McKenzie C A, Brodsky E, Brittain J H, Reeder    S B. Multiecho water-fat separation and simultaneous R2* estimation    with multifrequency fat spectrum modeling. Magn Reson Med 2008;    60(5):1122-1134.-   13. Liu J, Drangova M. Method for B0 off-resonance mapping by    non-iterative correction of phase-errors (B0-NICE). Magn Reson Med    2015; 74(4):1177-1188.-   14. Hosseini Z, Liu J, Solovey I, Menon R S, Drangova M.    Susceptibility-weighted imaging using inter-echo-variance channel    combination for improved contrast at 7 tesla. J Magn Reson Imaging    2016.-   15. Zhou D, Liu T, Spincemaille P, Wang Y. Background field removal    by solving the Laplacian boundary value problem. NMR Biomed 2014;    27(3):312-319.-   16. Marques J, Bowtell R. Application of a Fourier-based method for    rapid calculation of field inhomogeneity due to spatial variation of    magnetic susceptibility. Concepts in Magn Reson Part B 2005;    25B(1):65-78.-   17. Liu J, Drangova M. Intervention-based multidimensional phase    unwrapping using recursive orthogonal referring. Magn Reson Med    2012; 68(4):1303-1316.

Therefore what is claimed is:
 1. A method for Dixon MRI, multi-contrastimaging and multi-parametric mapping with a single multi-echo gradientecho (GRE) acquisition, comprising: a) performing unipolar or bipolarmulti-echo GRE acquisition including a short echo time (TE) train and along TE train; b) performing phase error correction associated with thebipolar acquisition over all echoes if bipolar acquisition is employed;c) performing Dixon MRI for the short-TE train data on a magnitude-basedFF proton density fat fraction (FF) map, an R2* map, a B0 field(Δf_(B0)) map and a complex-based FF map; d) performing averaging overthe magnitude images for the short-TE train to generate an anatomicalimage; e) processing the magnitude signal of all echoes to generate anR2* map; f) performing phase unwrapping of each individual echo exceptthe first echo; g) calculating susceptibility weighted imaging (SWI) onan echo-by echo basis based on the unwrapped phase; h) performing localfrequency shift (LFS) mapping; i) performing quantitative susceptibilitymapping (QSM); j) performing averaging of the SWI, LFS and QSM over theshort-TE train and the long-TE train separately; k) performing fatsuppression to the anatomical images, SWI images, LFS and QSM maps. 2.The step of claim 1 wherein performing multi-echo GRE acquisitioncomprises: a) selecting TEs optimized for Dixon MRI with a short firstecho time and tight echo spacing (the short-TE train); b) selecting TEsoptimized for susceptibility mapping while keeping fat and waterapproximately in-phase based on a multi-peak fat model (the long-TEtrain).
 3. The step of claim 1 wherein performing phase-error correctioncomprises: a) constructing a complex data set using Hermitian product;b) performing 3D phase unwrapping of the reconstructed complex data set;c) performing polynomial fitting of the unwrapped phase; d) performingphase-error correction to the odd and even echoes separately.
 4. Thestep of claim 1 wherein performing Dixon Mill for the short-TE traindata comprises: a) generating the magnitude-based FF and R2* maps usingmagnitude images based on a multi-peak fat model; b) generating a B0field map and a proton density fat fraction (FF) map using complex databased on a multi-peak fat model.
 5. The step of claim 1 whereingenerating anatomical images comprises: a) averaging the magnitudeimages of the short-TE train.
 6. The step of claim 1 wherein performingR2* mapping using all echoes comprises: a) correcting the magnitudeimages for fat-effects (using the FF) based on a multi-peak fat model;b) fitting the corrected magnitude images with a single-exponentialcurve.
 7. The step of claim 1 wherein performing phase unwrappingcomprises: a) calculating the Hermitian product between the later andthe first echoes; b) removing the phase term related to (Δf_(B0)),resulting in an intermediate complex data set; c) performing 3D phaseunwrapping to the intermediate complex data set; d) generating the finalunwrapped phase by summing up the unwrapped phase for the intermediatecomplex data set and the Δf_(B0)-related phase, followed by performingdeblurring.
 8. The step of claim 1 wherein performing SWI comprises: a)performing high-pass filtering of the unwrapped phase; b) generating aphase mask from the filtered phase; c) multiplying the magnitude imagesby the phase mask multiple times; d) performing the above procedures onan echo-by-echo basis.
 9. The step of claim 1 wherein performing LFSmapping comprises: a) generating a brain mask from the magnitude-basedFF maps; b) performing background phase removal c) generating the finalLFS map by converting phase to frequency; d) performing the aboveprocedures on an echo-by-echo basis.
 10. The step of claim 1 whereinperforming QSM comprises: a) performing dipole inversion of the LFSmaps; b) performing the above procedure on an echo-by-echo basis. 11.The step of claim 1 wherein performing fat suppression comprises: a)multiplying the generated images and maps by (1−FF).
 12. The step ofclaim 9 wherein generating a brain mask comprises: a) performing 3DGuassian filtering to the magnitude-based FF maps; b) thresholding thefiltered FF maps.